Agent skill

Supabase Performance Tuning

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Optimize Supabase query performance with indexes, EXPLAIN ANALYZE, connection pooling, column selection, pagination, RPC functions, materialized views, and diagnostics.

MITAuto-check passedDatabases

Install Supabase Performance Tuning

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill supabase-performance-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace supabase-performance-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/supabase-performance-tuning .claude/skills/supabase-performance-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
supabase-performance-tuning
GitHub stars
2.8k
Token cost
~3.2k tokens
SKILL.md length
675 words
Files
4 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Optimize Supabase query performance with indexes, EXPLAIN ANALYZE, connection pooling, column selection, pagination, RPC functions, materialized views, and diagnostics.

  • Works in 3 steps: Diagnose — Find What Is Slow → Indexes and Query Plans → Client SDK and Infrastructure Optimization
  • Queries are slow
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 4 more sections
  • Calls npx; reaches esm.sh; needs SUPABASE_ANON_KEY and SUPABASE_SERVICE_ROLE_KEY

What it does

Supabase Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Supabase query performance with indexes, EXPLAIN ANALYZE, connection pooling, column selection, pagination, RPC functions, materialized views, and diagnostics. Use when queries are slow, connections are exhausted, response payloads are bloated, or when preparing a Supabase project for production-scale traffic. Trigger with phrases like "supabase performance", "supabase slow queries", "optimize supabase", "supabase index", "supabase connection pool", "supabase pagination", "supabase explain analyze".

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/caching-strategy.md`, `references/errors.md` and `references/examples.md`). Compatibility notes: Designed for Claude Code

It sits in Databases, covering Query optimization. It works with Supabase. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Queries are slow
  • Connections are exhausted
  • Response payloads are bloated
  • Preparing a Supabase project for production-scale traffic

Example prompts

  • “supabase performance”
  • “supabase slow queries”
  • “optimize supabase”
  • “/supabase-performance-tuning”

Requirements

  • Node.js
  • A credential in SUPABASE_ANON_KEY
  • A credential in SUPABASE_SERVICE_ROLE_KEY
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npx:supabase), Bash(supabase:*), Grep

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Diagnose — Find What Is Slow
  2. Indexes and Query Plans
  3. Client SDK and Infrastructure Optimization

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(npx:supabase)
    • Bash(supabase:*)
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • esm.sh

    Also links to:

    • supabase.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • SUPABASE_ANON_KEY
    • SUPABASE_SERVICE_ROLE_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Supabase Performance Tuning loads about 3.2k tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 135 tokens; SKILL.md has 675 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~135
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 675 words, ~3,163 tokens.

Download SKILL.mdSave it as .claude/skills/supabase-performance-tuning/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
supabase-performance-tuning
description
Optimize Supabase query performance with indexes, EXPLAIN ANALYZE, connection pooling, column selection, pagination, RPC functions, materialized views, and diagnostics. Use when queries are slow, connections are exhausted, response payloads are bloated, or when preparing a Supabase project for production-scale traffic. Trigger with phrases like "supabase performance", "supabase slow queries", "optimize supabase", "supabase index", "supabase connection pool", "supabase pagination", "supabase explain analyze".
allowed-tools
Read, Write, Edit, Bash(npx:supabase), Bash(supabase:*), Grep
compatibility
Designed for Claude Code
version
1.54.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, supabase, performance, optimization, postgres

Supabase Performance Tuning

Overview

Systematically improve Supabase query and database performance across three layers: PostgreSQL engine (indexes, query plans, materialized views), Supabase infrastructure (Supavisor connection pooling, Edge Functions, read replicas), and client SDK patterns (column selection, pagination, RPC functions). Every technique here is measurable — run EXPLAIN ANALYZE before and after to confirm the improvement.

Prerequisites

  • Supabase project (local or hosted) with @supabase/supabase-js v2+ installed
  • Supabase CLI installed (npx supabase --version to verify)
  • Access to the SQL Editor in the Supabase Dashboard or a direct Postgres connection
  • pg_stat_statements extension enabled (Step 1 covers this)

Instructions

Step 1: Diagnose — Find What Is Slow

Start every performance effort with data. Enable pg_stat_statements and run the Supabase CLI diagnostics to identify bottlenecks before optimizing.

Enable the stats extension:

sql
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

Find the slowest queries by average execution time:

sql
SELECT
  query,
  calls,
  mean_exec_time::numeric(10,2) AS avg_ms,
  total_exec_time::numeric(10,2) AS total_ms,
  rows
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 10;

Check index usage and cache hit rates with the Supabase CLI:

bash
# Which indexes are actually being used?
npx supabase inspect db index-usage

# What percentage of queries are served from cache vs disk?
npx supabase inspect db cache-hit

# Tables consuming the most space
npx supabase inspect db table-sizes

Inspect active connections for pooling issues:

sql
SELECT state, count(*), max(age(now(), state_change)) AS max_age
FROM pg_stat_activity
WHERE datname = current_database()
GROUP BY state;

If idle connections exceed your plan's limit or active queries show high max_age, connection pooling (Step 2) and query optimization (Step 3) are the priority.

Step 2: Indexes and Query Plans

Indexes are the single highest-impact optimization. Use EXPLAIN ANALYZE to read query plans, then create targeted indexes.

Read a query plan:

sql
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT * FROM users WHERE email = 'alice@example.com';

Look for Seq Scan on large tables — that means no index is being used. After adding an index, the plan should show Index Scan or Index Only Scan.

Create a basic index:

sql
CREATE INDEX idx_users_email ON users(email);

Create a composite index for multi-column filters:

sql
-- Optimizes: WHERE user_id = ? AND created_at > ? ORDER BY created_at DESC
CREATE INDEX idx_orders_user_created
  ON orders(user_id, created_at DESC);

Create a partial index to cover a common filter pattern:

sql
-- Only indexes incomplete todos — much smaller and faster than full-table index
CREATE INDEX idx_todos_user_incomplete
  ON todos(user_id, inserted_at DESC)
  WHERE is_complete = false;

Find missing indexes on foreign keys (common source of slow JOINs):

sql
SELECT
  tc.table_name,
  kcu.column_name AS fk_column,
  'CREATE INDEX idx_' || tc.table_name || '_' || kcu.column_name
    || ' ON public.' || tc.table_name || '(' || kcu.column_name || ');' AS fix
FROM information_schema.table_constraints tc
JOIN information_schema.key_column_usage kcu
  ON tc.constraint_name = kcu.constraint_name
LEFT JOIN pg_indexes i
  ON i.tablename = tc.table_name
  AND i.indexdef LIKE '%' || kcu.column_name || '%'
WHERE tc.constraint_type = 'FOREIGN KEY'
  AND tc.table_schema = 'public'
  AND i.indexname IS NULL;

Find unused indexes (candidates for removal to reduce write overhead):

sql
SELECT schemaname, relname, indexrelname, idx_scan
FROM pg_stat_user_indexes
WHERE idx_scan = 0 AND schemaname = 'public'
ORDER BY pg_relation_size(indexrelid) DESC;

Always use CREATE INDEX CONCURRENTLY on production tables to avoid locking writes during index creation.

Step 3: Client SDK and Infrastructure Optimization

Optimize the Supabase JS client calls, then leverage infrastructure features for scale.

Select only needed columns — avoid select('*'):

typescript
import { createClient } from '@supabase/supabase-js'

const supabase = createClient(
  process.env.SUPABASE_URL!,
  process.env.SUPABASE_ANON_KEY!
)

// BAD: fetches every column, large payloads
const { data } = await supabase.from('users').select('*')

// GOOD: only the columns you need
const { data } = await supabase.from('users').select('id, name, avatar_url')

Paginate with .range() instead of loading all rows:

typescript
// Page 1: rows 0-49
const { data: page1 } = await supabase
  .from('products')
  .select('id, name, price')
  .order('created_at', { ascending: false })
  .range(0, 49)

// Page 2: rows 50-99
const { data: page2 } = await supabase
  .from('products')
  .select('id, name, price')
  .order('created_at', { ascending: false })
  .range(50, 99)

Use RPC functions to push complex logic to Postgres:

sql
-- Create a server-side function for an expensive aggregation
CREATE OR REPLACE FUNCTION get_dashboard_stats(org_id uuid)
RETURNS json AS $$
  SELECT json_build_object(
    'total_users', (SELECT count(*) FROM users WHERE organization_id = org_id),
    'active_projects', (SELECT count(*) FROM projects WHERE organization_id = org_id AND status = 'active'),
    'tasks_completed_30d', (SELECT count(*) FROM tasks t
      JOIN projects p ON p.id = t.project_id
      WHERE p.organization_id = org_id
      AND t.completed_at > now() - interval '30 days')
  );
$$ LANGUAGE sql STABLE;
typescript
// One network call instead of three separate queries
const { data } = await supabase.rpc('get_dashboard_stats', {
  org_id: 'your-org-uuid'
})

Create materialized views for expensive aggregations:

sql
-- Precompute a leaderboard instead of recalculating on every request
CREATE MATERIALIZED VIEW leaderboard AS
SELECT
  u.id,
  u.username,
  count(t.id) AS tasks_completed,
  rank() OVER (ORDER BY count(t.id) DESC) AS rank
FROM users u
LEFT JOIN tasks t ON t.assignee_id = u.id AND t.status = 'done'
GROUP BY u.id, u.username;

-- Create an index on the materialized view
CREATE UNIQUE INDEX idx_leaderboard_user ON leaderboard(id);

-- Refresh on a schedule (e.g., via pg_cron or a cron Edge Function)
REFRESH MATERIALIZED VIEW CONCURRENTLY leaderboard;

Configure connection pooling with Supavisor:

typescript
// For serverless environments (Vercel, Netlify, Cloudflare Workers):
// Use the pooled connection string with transaction mode
// Dashboard → Settings → Database → Connection string → "Transaction mode"

// The JS SDK uses PostgREST (HTTP) which has its own pooling — no config needed.
// Direct Postgres clients (Prisma, Drizzle, pg) need the pooled string:
import { Pool } from 'pg'

const pool = new Pool({
  connectionString: process.env.SUPABASE_DATABASE_URL,
  max: 5,  // Keep low in serverless — Supavisor manages the upstream pool
  idleTimeoutMillis: 10000,
})

Use Edge Functions for compute-heavy operations close to data:

typescript
// supabase/functions/generate-report/index.ts
// Edge Functions run in the same region as your database — low latency
import { createClient } from 'https://esm.sh/@supabase/supabase-js@2'

Deno.serve(async (req) => {
  const supabase = createClient(
    Deno.env.get('SUPABASE_URL')!,
    Deno.env.get('SUPABASE_SERVICE_ROLE_KEY')!
  )

  // Heavy aggregation runs next to the database, not in the user's browser
  const { data } = await supabase.rpc('get_dashboard_stats', {
    org_id: (await req.json()).org_id
  })

  return new Response(JSON.stringify(data), {
    headers: { 'Content-Type': 'application/json' }
  })
})

Enable read replicas on Pro+ plans for read-heavy workloads — route analytics and reporting queries to the replica to offload the primary.

Show full SKILL.md (306 more words)Show less

Output

After completing these steps, you will have:

  • Diagnostic baseline from pg_stat_statements, index-usage, and cache-hit
  • Targeted indexes on slow query columns, foreign keys, and common filter patterns
  • Query plans verified with EXPLAIN ANALYZE showing Index Scan instead of Seq Scan
  • Client queries optimized with column selection, pagination, and joined queries
  • RPC functions and materialized views for expensive server-side aggregations
  • Connection pooling configured via Supavisor for serverless deployments
  • Edge Functions deployed for compute-heavy operations near the database

Error Handling

SymptomCauseFix
Seq Scan in EXPLAIN output on large tableMissing index on filtered/sorted columnCREATE INDEX on the column(s) in the WHERE/ORDER BY clause
PGRST000: could not connect to serverConnection pool exhaustedSwitch to Supavisor pooled connection string; reduce max pool size in serverless
Slow RLS policies (visible in pg_stat_statements)Subquery in policy evaluates per rowRefactor to security definer function or use EXISTS instead of IN
Response payloads > 1MBselect('*') returning all columns/rowsUse .select('col1, col2') and .range() for pagination
Stale materialized view dataView not refreshed after writesSet up pg_cron or a cron Edge Function to run REFRESH MATERIALIZED VIEW CONCURRENTLY
cache-hit ratio below 99%Working set exceeds RAM (shared_buffers)Upgrade compute add-on or optimize queries to access fewer pages
High latency on aggregation endpointsAggregation computed live on every requestMove to materialized view or RPC function; cache at the Edge Function layer

Examples

Before/after index optimization:

sql
-- Before: 450ms, Seq Scan
EXPLAIN (ANALYZE) SELECT * FROM orders WHERE customer_id = 'abc-123';
-- Seq Scan on orders  (cost=0.00..15234.00 rows=50 width=128) (actual time=0.015..450.123 rows=50 loops=1)

CREATE INDEX idx_orders_customer ON orders(customer_id);

-- After: 0.8ms, Index Scan
EXPLAIN (ANALYZE) SELECT * FROM orders WHERE customer_id = 'abc-123';
-- Index Scan using idx_orders_customer on orders  (cost=0.42..8.44 rows=50 width=128) (actual time=0.025..0.812 rows=50 loops=1)

Client query optimization — eliminating N+1:

typescript
// BAD: N+1 — one query per project (10 projects = 11 queries)
const { data: projects } = await supabase.from('projects').select('id, name')
for (const project of projects!) {
  const { data: tasks } = await supabase
    .from('tasks').select('*').eq('project_id', project.id)
}

// GOOD: Single query with embedded join (1 query total)
const { data } = await supabase
  .from('projects')
  .select('id, name, tasks(id, title, status)')
  .eq('organization_id', orgId)

Resources

Next Steps

  • For RLS policy design, see supabase-rls-policies
  • For cost optimization, see supabase-cost-tuning
  • For real-time subscriptions, see supabase-realtime

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/.curated/supabase-performance-tuning of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/caching-strategy.md
  • references/errors.md
  • references/examples.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Supabase Performance Tuning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Supabase Performance Tuning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Supabase Performance Tuning this skilljeremylongshore/tons-of-skills-marketplace2.8k—~3.2kAutomated safety check: PassMIT
Supabase Postgres Best Practicessupabase/agent-skills2.7k24 repos~808Automated safety check: PassMIT
Database Domain Specialistmodu-ai/moai-adk1.2k—~2.8kAutomated safety check: PassApache-2.0
Postgres Patternsaffaan-m/ECC276k—~1kAutomated safety check: PassMIT
Postgresmagnus919/agent-skills115—~4kAutomated safety check: PassMIT
SQL Optimization Patternsynulihao/AgentSkillOS61811 repos~3.3kAutomated safety check: PassNone

Similar skills

  • Official

    Gives the agent Postgres rules to consult before writing or changing tables, queries, indexes, RLS policies or migrations, and when diagnosing slow queries.

    2.7k GitHub starsUsed in 24 repos~808 tokens
    DatabasesAuto-check passed
  • Database guidance for PostgreSQL, MongoDB, Redis and Oracle plus Neon, Supabase and Firestore: schema design, indexing, query tuning and cloud database choice.

    1.2k GitHub stars~2.8k tokensUpdated 2 days ago
    DatabasesAuto-check passed
  • Postgres Patterns

    affaan-m/ECC

    PostgreSQL database patterns for query optimization, schema design, indexing, and security.

    276k GitHub stars~1k tokensUpdated yesterday
    DatabasesAuto-check passed
  • Postgres

    magnus919/agent-skills

    Operate PostgreSQL instances safely: configuration review, index and query-plan analysis, vacuum and bloat management, WAL archiving and point-in-time recovery, replication and failover, extensions…

    115 GitHub stars~4k tokensUpdated yesterday
    DatabasesAuto-check passed
  • SQL Optimization Patterns

    ynulihao/AgentSkillOS

    Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries.

    618 GitHub starsUsed in 11 repos~3.3k tokens
    DatabasesAuto-check passed
  • Official

    Query Cloud Trace spans, filter by latency thresholds or error status, correlate distributed traces with Cloud Logging, and diagnose latency bottlenecks across Google Cloud services.

    21k GitHub stars~1.7k tokensUpdated yesterday
    DatabasesAuto-check passed

More from jeremylongshore/tons-of-skills-marketplace

All 3,342 skills in this repo
  • Performing Security Code Review

    jeremylongshore/tons-of-skills-marketplace

    Execute this skill enables AI assistant to conduct a security-focused code review using the security-agent plugin.

    2.8k GitHub starsUsed in 2 repos~1.3k tokens
    Auto-check: notes
  • Adapting Transfer Learning Models

    jeremylongshore/tons-of-skills-marketplace

    Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques.

    2.8k GitHub stars~1.1k tokensUpdated today
    Auto-check passed
  • Agent Context Loader

    jeremylongshore/tons-of-skills-marketplace

    Execute proactive auto-loading: automatically detects and loads agents.md files.

    2.8k GitHub stars~1.1k tokensUpdated today
    Auto-check passed
  • Aggregating Performance Metrics

    jeremylongshore/tons-of-skills-marketplace

    Aggregate and centralize performance metrics from applications, systems, databases, caches, and services.

    2.8k GitHub stars~1.2k tokensUpdated today
    Auto-check passed
  • Analyzing Capacity Planning

    jeremylongshore/tons-of-skills-marketplace

    Execute this skill enables AI assistant to analyze capacity requirements and plan for future growth.

    2.8k GitHub stars~947 tokensUpdated today
    Auto-check passed
  • Analyzing Database Indexes

    jeremylongshore/tons-of-skills-marketplace

    Process use when you need to work with database indexing. An agent skill from jeremylongshore/tons-of-skills-marketplace.

    2.8k GitHub stars~2k tokensUpdated today
    Auto-check passed

Works with

Categories

Questions about Supabase Performance Tuning

What does Supabase Performance Tuning do?

Optimize Supabase query performance with indexes, EXPLAIN ANALYZE, connection pooling, column selection, pagination, RPC functions, materialized views, and diagnostics. Supabase Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Supabase query performance with indexes, EXPLAIN ANALYZE, connection pooling, column selection, pagination, RPC functions, materialized views, and diagnostics.

When should I use Supabase Performance Tuning?

Supabase Performance Tuning fits situations like: queries are slow; connections are exhausted; response payloads are bloated; preparing a Supabase project for production-scale traffic.

How do I install Supabase Performance Tuning in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill supabase-performance-tuning -a claude-code`. Or copy the skill folder (skills/.curated/supabase-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/supabase-performance-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Supabase Performance Tuning in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill supabase-performance-tuning -a codex`. Or copy the skill folder (skills/.curated/supabase-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/supabase-performance-tuning in your project. Codex loads it when a task matches its description.

Can I use Supabase Performance Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill supabase-performance-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/supabase-performance-tuning, .gemini/skills/supabase-performance-tuning, .github/skills/supabase-performance-tuning and .opencode/skills/supabase-performance-tuning in your project.

What does Supabase Performance Tuning need to run?

Going by SKILL.md and its folder, Supabase Performance Tuning needs the command-line tools its instructions call (npx) and credentials named SUPABASE_ANON_KEY and SUPABASE_SERVICE_ROLE_KEY. Our summary lists: Node.js; A credential in SUPABASE_ANON_KEY; A credential in SUPABASE_SERVICE_ROLE_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(npx:supabase), Bash(supabase:*), Grep. Compatibility (from SKILL.md): Designed for Claude Code.

Does Supabase Performance Tuning access the network?

SKILL.md names 2 domains. In commands or code: esm.sh; the agent is likely to contact it when it follows the instructions. As links in the text: supabase.com. This is read from the text; nothing was executed.

Is Supabase Performance Tuning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Supabase Performance Tuning use?

Supabase Performance Tuning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Supabase Performance Tuning use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 449 tokens, read only when the agent opens those files.

What are the alternatives to Supabase Performance Tuning?

Skills that share tags, products or a category with Supabase Performance Tuning: Supabase Postgres Best Practices (supabase/agent-skills, 2.7k stars), Database Domain Specialist (modu-ai/moai-adk, 1.2k stars), Postgres Patterns (affaan-m/ECC, 276k stars) and Postgres (magnus919/agent-skills, 115 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Supabase Performance Tuning?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.